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Indirect matrix converters (IMC) feature an AC-AC configuration without an intermediate DC link and wide application prospects in wind power generation, AC transmission, and various other fields, known for their sinusoidal input/output, high power density, and superior operating efficiency. However, these converters inevitably generate common-mode voltages during modulation operations, causing motor damage and affecting the reliable functioning of the systems. This paper aims to enhance the operational reliability of IMCs by suppressing common-mode voltage output, and focuses on five classical modulation methods for suppressing common-mode voltage peaks by 42.3% in IMCs, including the modulation method for synthesizing reference output voltage vectors using adjacent four effective voltage vectors at the inverter stage, that for synthesizing reference output voltage vectors using adjacent three effective voltage vectors at the inverter stage, that for synthesizing reference current vectors using adjacent three effective current vectors at the rectifier stage, that for synthesizing reference current vectors using two adjacent 120° effective current vectors plus a zero-current vector at the rectifier stage, and that for synthesizing reference current vectors using weighted three effective current vectors at the rectifier stage. By modeling the effective value (root mean square, rms) of output common-mode voltages, which incorporates the influence of voltage transfer ratios on these values, a quantitative analysis was conducted to explore the variation patterns of the rms value of common-mode voltages. Following this, an evaluation was performed on the suppression capability of different modulation methods on common-mode voltages. The comparative analysis results indicated that the space vector modulation method of synthesizing reference voltage using adjacent four effective voltage vectors at the inverter stage exhibited the best suppression effect on the rms value of common-mode voltages in IMCs. Additionally, the correctness of the theoretical analysis results was verified through simulations and experiments.

Virtual coupling of urban rail train sets represents a cutting-edge frontier in urban rail transit research, with the goal of improving operational efficiency of trains on the existing lines, particularly in the starting and stopping stages. This paper proposes an efficient collaborative control system for virtual coupling. Firstly, a model was constructed to simulate relative braking between urban rail train sets and it was used to generate relative braking distances between trains virtually coupled. Next, an efficient starting and stopping objective function was constructed at the collaborative control layer, and an optimized controller was devised to solve the objective function under constraints, generating key decision-making information for the leading and following trains during the starting and stopping stages. Subsequently, this study focused on the operation sequence planning technology, yielding the executable operation sequences of the leading and following trains, respectively. Finally, an LQR controller was designed, enabling the accurate following and control of operation sequences and the generation of execution instructions. Simulation results showed that the proposed control system for virtual coupling could achieve a parking time interval of less than 1.6s between the leading and following trains in urban rail transit within a speed range of 0~60 km/h, effectively improving the operational efficiency of virtually coupled urban rail train sets during the starting and stopping stages.

At present, the locomotive automatic driving device has been applied to multiple lines and has achieved certain phased results. Due to factors such as complex application scenarios, strong non-linearity and discreteness of control objects, some problems and challenges have arisen during the operation assessment, including impulsive and surging phenomena during train interval operation, non-compliance with station benchmark parking accuracy requirements, failure to start on steep slopes, and difficulty in evaluating air braking performance. These problems seriously affect the smoothness of operation and driving safety. The article studies the automatic driving control technology for key scenarios. Through in-depth analysis of operating procedures and historical operating data, optimization of train dynamics model parameters, establishment of accurate basic resistance models and air braking models, design of expert knowledge base for smooth operation strategy, intelligent parking control strategy, air braking terminal predictive control method, model and data-driven safety evaluation method, the problems encountered in on-site operation are ultimately solved. By the proposed strategies, it achieves the goal of 100% fully automatic control for the main line operation of 20 000 ton combined trains, providing a technical foundation for the normalized application of locomotive automatic driving.

Standardization is one of the key steps to analyze locomotive overhaul data with a focus on reliability-centered maintenance (RCM). However, traditional manual methods encounter challenges such as small sample sizes, non-standardized data formats, analytical complexities, and high labour costs, hindering the achievement of data standardization. Large language models (LLM), featuring powerful performance in natural language processing comprehension and handling complex tasks, have made great academic and industrial progress in recent years. This study initially investigated the application performance of LLMs in information extraction from locomotive overhaul data, with the following three reveals, as the universal information extraction (UIE) LLM is suitable for information extraction in the field of locomotive overhaul; expanding the size of locomotive data helps improve the UIE performance in information extraction from locomotive overhaul data; balancing the types of fault labels does not notably help improve this performance. Subsequent explorations concentrated on difficulties in data annotation. The script writing method was utilized for automated annotation of data, and ChatGLM was leveraged to standardize locomotive overhaul data, yielding Bleu-4, Rouge-1, Rouge-2, and Rouge-L metrics of 86.87%, 89.60%, 87.54%, and 94.26%, respectively, in alignment with the requirements of engineering applications. Further developments introduced an auxiliary data standardization pre-processing tool to streamline the standardization process by encapsulating the LLM.

Scenarios such as tidal passenger flow, instantaneous large passenger flow, and mixed operation demand an increasing number of autonomous-rail rapid trams (ART) to increase passenger capacity. However, traditional mechanical physical coupling proves too cumbersome for convenient adjustments. This paper proposes a virtual coupling concept and system architecture for autonomous-rail rapid trams as a solution. This study developed a safe braking model and collaborative control model, in addition to establishing a collaborative planning objective function and constraints. Real-time calculations of speeds, accelerations and traction/braking force percentages for the following ART were conducted under the condition of maintaining the minimum safe distance. Simulations and experiments conducted with real trams demonstrated distances between the trams of less than 5 m, arrival time differences of less than 3 s, parking time differences between the leading and following trams of less than 0.5 s, jerks of the following tram not exceeding 0.4 m/ s3, as well as seamless switching between traction and braking during the operation of virtually coupled trams at 45 km/h. These results underscored the effectiveness of the proposed system in facilitating punctual, precise, comfortable and energy-efficient operation of virtually coupled trams. This research lays a solid foundation for potential future applications of virtually coupled ARTs at scale.

In some low-cost industrial application scenarios, serial communication is mostly transmitted in plaintext, leaving data vulnerable to theft and issues, such as poor data reliability. To enhance data security during transmission and communication reliability, this paper proposes an encrypted serial communication system based on pseudo-random sequences. The system adopts the technologies of arbitrary series pseudo-random sequence generation, dynamic sampling window adjustment, and serial code stream scrambling to realize non-transparent data transmission via channels. At the receiving end, pseudo-random sequences are synchronized through capturing the header, allowing for serial code stream decoding and data information acquisition. Within an environment of the communication system created by using field-programmable gate array (FPGA), experiments revealed the maximum baud rate at 20 Mbit/s, which could meet the encryption requirements of m-sequences of 4~15 series, and yielded good results.

Sliding-mode controllers, typically featuring variable structures and a discontinuous mode, have been widely applied due to their robustness and straightforward structures. However, their switching characteristics also introduce adverse effects such as chattering and switching delays. This paper focuses on explaining and analyzing chattering in sliding-mode control systems from novel perspectives surrounding hybrid dynamics and Zeno behavior. An initial analysis on an ideal sliding-mode control system capable of switching control signals instantaneously at any frequency, revealed the sufficient conditions for Zeno behavior occurrences. Subsequent investigations incorporated constraints of switching devices in two types of sliding-mode controllers in real applications: those with a lower bound on positive switching intervals and those exhibiting hysteresis. Building upon the theoretical results related to the Zeno behavior in the ideal sliding-mode controller, this study examined the correlation between chattering in these two types of sliding-mode control systems and the Zeno behavior of the ideal sliding-mode controller. The study results finally demonstrated the impracticality of ideal sliding-mode controllers and the inevitability of chattering in sliding-mode controllers in practical applications.

This paper presents a method to establish typical operating conditions for train traction systems by combining site-based short trip analysis with nonparametric tests, in order to overcome various obstacles, such as inadequate efficiency testing results based on rated points of train traction systems, which often fail to accurately reflect the actual energy efficiency level of systems during long-distance operation under multiple conditions, and the existing absence of typical operating conditions essential for supporting the evaluation of traction systems' energy efficiency levels and guiding the energy-saving design of products. First, short trips were categorized, screened and randomly combined. Then, the optimal short-trip combination was identified through nonparametric tests. Subsequently, the typical operating conditions were developed, taking the traction system of an electric locomotive model as a case study. Furthermore, the proposed method was verified by analyzing errors in operating characteristic parameters and assessing the similarities in velocity-force (V-F) distribution probability two-dimensional matrix. The verification results revealed a deviation between the constructed typical operating conditions and the characteristic values derived from sampled data at 4.54%, and the consistency in V-F distribution probabilities at 98.06%. These findings indicate that the method proposed in this paper accurately reflects the actual operating characteristics of train traction systems, with the established typical operating conditions closely aligning with the actual operating conditions of train traction systems, underscoring their potential for aiding in energy-saving design and energy efficiency evaluation for train traction systems.

To improve the testing efficiency of computer interlocking systems and shorten the testing cycle of urban rail transit projects, this paper proposes an automated testing method for interlocking data of signaling systems for urban rail transit. Built upon testing scripts, this method is devised to describe testing scripts using non-programming languages, allowing for the control of interlocking software without modifying the software itself or integrating additional communication interfaces. This data-driven approach automatically generates testing scripts, utilizing data such as interlocking tables and interface code tables. Following the parsing process of testing scripts, it enables the transmission of control commands to the interlocking system, state collection of tested devices, and determination of testing results. The proposed method has been applied in the construction of the west extension of Line 2 (Phase II) in the Changsha Urban Rail Transit. Through comparing the results from automated and manual testing in this project, the automated testing approach exhibited an overall defect recognition rate of 85%, particularly a defect recognition rate of 100% for interlocking tables, and a decrease in actual time consumption of 76% in comparison with the manual mode, verifying the effectiveness of the method. This method has also contributed to a significant improvement in testing efficiency.

The rapid growth of the urban rail transit industry has resulted in a surge in the volume of operational data generated by urban rail trains. However, traditional full-data storage methods have become inadequate in handling the escalating demands for data processing and storage. To address this, this paper presents an innovative optimization strategy that utilizes the data hopping technology for data processing and storage. This strategy aims to significantly reduce processing demands for urban rail train operational data, minimizing the occupation of storage resources, by comparing data from adjacent time sequences to accurately identify and store characteristics of data changes. In subsequent site experiments, the proposed approach achieved a 97.5% reduction in data processing demands and a 97% saving in storage space, while ensuring data security and stability. The study outcomes not only support the endeavor to effectively ease the burden on data transmission and storage, but also provide a robust data foundation for the intelligent operation and maintenance of urban rail transit.

The magnitude of safe intervals between virtually coupled trains derived from the safe train interval model based on relative braking distance has a great impact on the track capacity planning, infrastructure construction and vehicle design. This study sought to analyze the sensitivity of key parameters affecting these safe intervals, to harness the potential of virtual coupling technology in improving efficiency while ensuring operational safety. The qualitative analysis based on a safe time interval model and the quantitative analysis based on simulations using the model plus an algorithm, were conducted, shedding light on the extent of influence from key parameters in the safe interval model on stopping time differences. The local sensitivity ranking of corresponding parameters was established using the one-factor-at-a-time method (OFAT), namely, from braking rate , response delay, "braking application" stage time to target stopping distance in a descending order. This ranking delineates the range and principle of selecting key parameters and the optimization priorities in safe interval design. In order to make stopping time differences controllable and less than 4 seconds, a 3-stage emergency braking mode was adopted for station approaching, and appropriate typical combination indexes were recommended: the braking rate of more than 1.0 m/s2 for the following train, the response delay of less than 1 s, the minimum safety distance of 2 m and the corresponding target stopping distance more than 4 m. The study results provide the basis for the engineering implementation of virtual coupling.

The all-vanadium redox flow technology has garnered significant attention in the energy storage field, due to its attributes of high safety, high reliability, environmental friendliness, and power-capacity decoupling. Within all-vanadium redox flow energy storage setups, the control system is deemed the core, evolving towards enhanced integration, precision, and intelligence. This paper seeks to elevate these benchmarks and advance technological levels in the localization of this system. Based on a review of technologies related to the entire control system, this study conducted an in-depth analysis concerning key parameters and control logic. Addressing some key concerns within the control system, solutions were proposed, including temperature control, operational modes, and state-of-charge (SOC) estimation. The subsequent study focused on designing architecture for monitoring and managing all-vanadium redox flow energy storage systems through a modular approach, aiming at a more adaptable, elastic, and scalable control system. Furthermore, the developed control system was verified through practical implementation in commercial energy storage and applications under grid-connected environments. Based on the study outcomes, this paper also explores potential future technological trends for all-vanadium redox flow energy storage control systems in various aspects such as operation, maintenance, losses, and equilibrium.

The intelligent navigation system of tourism parks typically functions to recommend diverse sightseeing paths for tourists of different age groups, comprehensively considering factors such as path distances and the geographical location of all the scenic spots. However, the classical ant colony algorithm often get stuck in local optima during path planning, alongside exhibiting low planning efficiency and a low convergence rate. This paper presents an improved ant colony algorithm combining the classical ant colony algorithm and the genetic algorithm, seeking to enhance the efficiency of the park navigation system in planning sightseeing paths. Initially, the algorithm generates initial pheromone distributions for identifying the optimal path, based on the genetic algorithm and through a strategy of crossover and mutation. Subsequently, the process entails distinct optimizations, utilizing the heuristic function, pheromone update mechanism, and state transition strategy within the ant colony algorithm, ultimately arriving at the optimal solution for the traveling salesman problem (TSP) and further refining paths. The experimental verification showed a path distance of 305.62 meters at the 87th convergence using the improved ACA+GA algorithm, with 250 repeated iterations at α=1.5, β=3, ρ=0.5, Q=300. In contrast, the classical ant colony algorithm resulted in a path distance of 519.74 meters by the 196th convergence. These results showcased the efficacy of the improved ant colony algorithm in overcoming the shortcomings of the classical ant colony algorithm, achieving a higher rate in reaching the optimal solution and an enhanced efficiency of path planning.

The virtual coupling technology of urban rail trains offers an effective solution to unbalanced temporal and spatial distribution of passenger flow. Based on the key requirements for urban rail trains, namely punctuality, comfort and sufficient time for platform operations, this study proposed a cooperative speed planning algorithm for virtual coupling, featuring information centralized decision-making, following an investigation into train traction and braking characteristics, vehicle kinematics and dynamics models. This algorithm was further devised to incorporate constraint conditions, objective functions, and optimal solutions, and then used to calculate the planning sequence of displacements, speeds, accelerations, traction or braking forces, jerks and operating conditions during the operation of two coupled trains over time. In subsequent field experiments, the arrival time differences of the coupled trains were less than 3 s, stopping time differences between the preceding and following trains were less than 2 s, jerks were maintained at less than 0.1 m/s3, and smooth switching was achieved between traction and braking conditions, demonstrating the efficacy of the proposed algorithm in facilitating safe, punctual, comfortable and efficient operation of coupled trains. The study outcomes lay a solid foundation for future engineering applications.

The conventional diagnostic logics for hotbox warning/alarms are mainly based on predefined absolute temperature thresholds and temperature rising slope thresholds. They often fall short in effective analysis and judgments when encountering fluctuating measured temperature values. Abnormal jumps and missing of axle temperature measurements frequently occur on the hotbox monitoring system for high-speed trains due to external factors such as inadequate hardware circuit contact or electromagnetic interference, potentially triggering false warning/alarms. To solve these, this paper proposes a logic framework for removing abnormal temperature curves. Drawing insights from an extensive analysis of field data on axle temperature measurements, this approach enables extracting features that accurately depict variations in temperature curves, to display fluctuations as well as rising trends of temperature data, thereby offering a basis for identifying abnormal temperature variations. Simulation results showcased the effectiveness and viability of the proposed scheme in identifying abnormal jumps in temperature data. Furthermore, the methodology has been assessed through long-time track operation, with a notable reduction in the false warning rate from 78.3% to 26.5%.

Synchronously sampling three-phase current in the traction control unit based on the control cycle is necessary to enhance control of permanent magnet synchronous motors. Simultaneously, continuous current sampling at small time intervals is essential for realtime over-current protection. This paper presents a solution based on a field-programmable gate array (FPGA), which incorporates a single set of analog-to-digital converter (ADC) chips to enable both synchronous current sampling essential for controlling permanent magnet synchronous motors and fast timed sampling necessary for real-time over-current protection. This design intelligently generates ADC conversion start signals through acquiring user-configured synchronous sampling periods in real time alongside timed sampling periods, thus enabling synchronous and timed current sampling control for permanent magnet synchronous motors. Finally, experiments were conducted on an actual permanent magnet traction control platform to verify the feasibility and effectiveness of the proposed scheme.

The rapid detection of grid voltage faults is crucial to achieve grid voltage fault ride-through. However, conventional grid voltage detection methods, including Fourier transform, dq axis transform, wavelet transform, and voltage peak methods, generally yield varying degrees of distortions in the detection results of short-time disturbances in grid voltage. To address this, this paper proposes an approach for quickly detecting short-time disturbances in grid voltage. Leveraging the distinctive high harmonic content present in grids, the suggested strategy involves a transformation of grid voltage calculated using the Fourier transform method, known for its robust anti-interference performance, into high-frequency signals through coordinate conversion. The integration of the rolling sliding window method addresses the extended calculation time issue associated with the Fourier transform, enabling the detection of grid voltage disturbance signals within a single high-frequency signal cycle using this method. The simulation experiment results demonstrated the wide applicability of the proposed method compared with other methods. It is not only effective for short-time grid voltage disturbances, but also suitable for long-time grid voltage disturbances. Regardless of whether high-frequency harmonics are present or absent in the grid voltage, this method allows for the rapid detection of grid voltage disturbances (within about 2 ms). Moreover, its accuracy in detecting grid voltage amplitudes aligns with requirements in engineering applications.

Open-pit mines, characterized by complicated environmental conditions and unstructured roads, pose challenges for the path planning of mine trucks, marked by inefficiency due to long-distance search and the need to adhere to the left-hand traffic rules common in mining settings. This paper introduces an optimization algorithm for adaptive dual-layer search path planning based on the left-hand traffic rules in mining areas, aiming to improve the navigation performance of mine trucks within these areas while ensuring the path safety and the real-time planning. The upper-layer search, employing the rapidly-exploring random tree (RRT) algorithm and accommodating the environmental characteristics of mining areas, features adaptive adjustments of search step sizes, achieving efficient searches with larger step sizes in open areas, while detailed searches with smaller step sizes in narrow or winding areas. Moreover, the search process is further optimized by setting range constraints for left-hand driving, to prevent potential vehicle conflicts and facilitate rapid convergence of path search. The lower-layer search incorporates a hybrid A* algorithm, effectively narrowing search space and enhancing the real-time nature of path planning through a reward and penalty mechanism for path selection. Furthermore, Reeds-Shepp (RS) curve and an improved cubic spline curve are applied to smooth the resultant path, not only optimizing path curvature but also ensuring that the mine truck reaches the target location in an optimal posture. Further optimization through the gradient descent method enables the efficient generation of safe and smooth paths. Experimental results showed a 14.8% reduction in path generation time and a 0.011 m-1 decrease in path curvature, demonstrating a significant enhancement in the navigation efficiency and safety during autonomous truck driving in mining areas.

Long wheelbase design of vehicle can effectively increase the standing area without increasing the body length, thus increasing the passenger capacity. Today, with the development of urbanization, the long wheelbase vehicle design has become a trend, but this poses new challenges to the low-speed trafficability and high-speed stability of vehicles. This paper takes the long wheelbase commercial vehicle as the research object. Based on the vehicle dynamics and suspension design theory, the author first designed key parameters of long wheelbase vehicle and built an 18 degrees of freedom (DOF) dynamics simulation model for the vehicle; then designed the four-wheel steering (4WS) control algorithm according to the design parameters of the vehicle, for achieving the control target that the side slip angle tends to zero; and finally researched the influence of front-wheel steering (FWS) control and four-wheel steering control on the dynamic performance of the vehicle under the steady-state circumferential conditions with different turning radiuses and steering wheel angle pulse conditions with different speeds. The simulation results show that, under the steady-state circumferential turning condition with a low-speed turning radius of R15, the four-wheel steering design reduces the passing space from 4.6 m to 3.9 m, effectively improving the tracking ability of the front and rear axles of the vehicle and enhancing the trafficability and safety of the vehicle, and under the pulse condition with a maximum speed of 100 km/h, reduces the peak lateral acceleration from 4 m/s2 to 1.5 m/s2 and the peak yaw rate from 11°/s to 3°/s. Therefore, under high-speed steering wheel angle pulse conditions, the four-wheel steering design can effectively reduce the dynamic indicators of the vehicle, such as side slip angle, lateral acceleration and yaw rate, and improve the safety, stability and comfort of the vehicle at high speed.

Affected by meteorological conditions, terrains, locations and specific designs, wind turbines exhibit significant uncertainties and disparities in wind energy input, which makes it difficult to predict their output power. This paper aims to enhance operational control balance in wind turbines and advance more sophisticated and intelligent control at wind farms. Utilizing the autoregressive integrated moving average (ARIMA) model, a component of time series analysis, this study analyzed time series data related to the micro wind speeds of wind turbines, and examined their correlation and randomness. The study results culminated in time series modeling to represent micro wind speeds of wind turbines, which facilitated the subsequent wind speed prediction trials. Through employing the algorithm developed for micro wind speed prediction of individual wind turbines at wind farms, the proposed approach provides supporting data for the vortex-induced vibration resistance, grid connection preparation, prevention of operational risks including load impacts, and precise control, establishing a framework for performance balance across wind turbines at wind farms, refined management including service life, and efficient operation and maintenance.

The Shenmu-Shuozhou Railway of Shenhua Baoshen Railway Group Co., Ltd. operates 10,000t heavy-haul trains using centralized traction on intricate railway conditions. The application of the automatic train operation (ATO) mode has accentuated train impulse issues arising from frequent traction adjustments during train operation, underscoring the need for enhanced stability of ATO. This paper presents an optimization control algorithm based on a dynamic evaluation model of train states, tailored to the specific conditions and operational requirements of the Shenmu-Shuozhou Railway. This algorithm features a multi-particle model and a train state evaluation model for heavy-haul trains, evaluating the development trend of train states while incorporating constraints related to the loading/unloading slope in levels. The proposed algorithm was applied in an experiment on the Shenmu-Shuozhou Railway. During the departing stage, the optimization algorithm effectively improved operational stability on steep grades, and increased the departing speed by about 11.1%. During the cruising stage, train impulses was effectively suppressed pre and post neutral-zone crossings, and the train running speed increased by about 0.9%. Moreover, the trend of simulated coupling force changes aligned closely with actual traction loading, and the maximum locomotive-wagon coupling force was reduced by about 26.3%. During the parking stage, the maximum amplitude of train acceleration decreased by 71.4%, and the average impulse count decreased by 60%. The experimental results demonstrate the effectiveness of the proposed algorithm in improving the ATO stability for 10,000t heavy-haul trains operating under centralized traction.

At present, The predominant method of anomaly detection for wind turbines remains manual "sound inspection" conducted by experienced operation and maintenance personnel, despite its low efficiency and poor timeliness. The application research on wind turbines anomaly detection is still in its early stages, despite the inherent characteristics of security, continuity, and stability associated with acoustic fault detection. Recognizing the audio fault detection characteristics , this paper presents a few shot anomaly detection method for wind turbines based on audio data, to support quick diagnosis by operation and maintenance staff. Firstly, a wind turbine audio fault detection system is established and the collected wind turbine fault audio data is preprocessed using a recurrent neural network (RNNoise). Then, in view of the deficiency of effective samples of wind turbine faults and the presence of pseudo samples, the compression and reconstruction anomaly detection method was used to facilitate few shot anomaly detection. Finally, pseudo faults were further distinguished by the reverse gradient transfer of anomaly data, leading to an enhancement of fault detection accuracy. Actual data collected in an experiment show a more than 95% recognition rate for abnormal sound arising from wind turbine yaw, demonstrating the viability of the proposed technique for deployment at wind farms. This application contributes to improving the intelligent technical level of wind farms.

At present, most of China's onshore wind farms use doubly-fed induction generators (DFIG) with small-capacity and and low-cost converters, but generally, these DFIGs can only operate within the slip rate range of ±30%, and can not be connected to the grid normally under low-speed conditions. In order to improve the output of DFIGs under low-speed conditions, the paper proposes a dual-mode grid-connection control scheme. The author first established an asynchronous mathematical model for stator shorting of doubly-fed motor to finally identify the value range of short-circuit resistance, and then put forward the dual-mode flexible switching strategy, that is, units operate in the asynchronous mode when the speed is low, and in the conventional doubly-fed mode when the speed is high, thus realizing power generation operation at low speed. The simulation results show that, at low speed, in the asynchronous mode, the rotor current distortion rate can be reduced by 50% and the power control accuracy is higher, with the power fluctuation amplitude controlled within 3 kW, compared with those in the doubly-fed mode, and after the flexible switching strategy is adopted, the transient impulse current can be reduced by more than 36% and the new steady state can be reached in only 150 ms. This verifies the validity and feasibility of the dual-mode grid-connection control strategy for DFIGs.

Complicated terrains, specific regions, and high wind turbulence intensity frequently trigger shutdown protection of wind turbines due to vibration beyond the designated limit, resulting in serious loss of power generation. This paper aims at reducing the frequency of shutdowns and improving operational stability of wind turbines. Operational characteristics of wind turbines under complicated conditions such as specific regions and strong turbulent wind were analyzed. A fuzzy control approach was proposed based on pertinent theory, to deal with complicated operating conditions at specific sites. This strategy incorporates a vibration recognition algorithm to classify vibration conditions and a fuzzy control algorithm for vibration suppression based on the categorized conditions. Compared with the conventional shutdown or sector-based control methods, the proposed fuzzy control strategy of vibration suppression eliminates the frequent starting and stopping of wind turbines, thus ensuring power generation and improving operational stability. Furthermore, a simulation analysis was performed using Bladed software, taking 3.x MW wind turbines as the research object. Subsequent experimental verification was carried out at a wind farm. In the experiment, the application of the fuzzy vibration suppression control method effectively led to the quick ride-through of the wind turbine amid the complicated operating conditions including the specific region and strong turbulent wind. A 23.87% reduction in the maximum RMS of the front and rear accelerations of the nacelle demonstrated a substantial enhancement in the wind turbine reliability.

Pressure compensators have a significant impact on the depth and performance of remotely operated vehicle (ROV) operations, necessitating the development of high performance and easily maintainable safety compensators. This paper delved into the research on the design of pressure compensators for ROVs, in order to enhance their engineering applications in deep-sea ROVs. By analyzing and comparing the weight of the ROV valve blocks and valve box shells made of different materials but exhibiting the same volume, it was found that the valve blocks and valve box shells made of stainless steel and titanium alloy met the pressure resistance requirements, in the absence of pressure compensators. However, the weight of the ROVs increased by about 187% and 74% respectively. The ROV, which adopted a fusion design of aluminum alloy valve blocks, valve box shells, and compensators, demonstrated excellent performance in light weight, compact structure, and deep water operations. A further examination was conducted using a rolling diaphragm pressure compensator as an example, focusing on structural characteristics and static performance. The results highlighted the role played by relevant elastic and structural components in adjusting the pressure difference between the compensation pressure and seawater pressure, until the required compensation pressure of the pressure compensator in the operation. In addition, based on the principles of rolling diaphragm pressure compensators such as oil contraction and expansion, this study introduced a volume calculation principle for pressure compensators, highlighting pressure and temperature as the important parameters that affect the compensator volume selection. The application of pressure compensators designed according to this principle can facilitate ROV operations in deep sea up to 5 000 m in depth (about 50 MPa), without the need of protection by additional thick pressure resistant structures. This paper concludes by drawing attention to prospects for the future development of pressure compensator technology from three aspects: sealing performance, structural design, and pressure compensation.

In order to get rid of technical barriers, ensure the safety of industrial control and realize the controllability of information security, it is necessary to focuses on the localized development and replacement application of controllers for wind turbine generators (WTG). Emphasizing practicality, integrity, real-time performance, reliability, stability, security and progressiveness, this study prioritized the adoption of the domestic Godson 2K1500 chip and real-time embedded operating system in the localized design and development of controllers to enhance safety, controllability and efficiency. The domestically developed controller was further configured with hardware and I/O point table aligned with the functional requirements of the WTG control system. Furthermore, the study analyzed the communication modes between the controller and interface devices, in addition to outlining a design for software control logic. The designed controller was subsequently incorporated into a WTG, which was put into trial operation for 240 hours after successful commissioning. During this acceptance testing, the system operated stably and reliably, with all performance indicators remaining compliant with national or industry standards. The control system for MW level WTGs based on the domestic controller featuring autonomous controllability was proven to be autonomously controllable, safe and reliable.

Multi-criteria priority ranking involves determining priorities through calculations for a multitude of tasks as per potentially conflicting criteria, to aid decision-makers in efficient allocation of resources and prioritization of tasks based on their criticality. This process is essential for optimizing resource allocation and improving overall work efficiency. Over the past two decades, the field of priority ranking has seen the development of numerous mature classic methods through continuous modifications and expansions. Furthermore, new sorting methods rooted in advancing technologies in the era of big data have also emerged. To assist decision-makers in evaluating and selecting the most suitable multi-criteria prioritization methods for priority ranking needs in specific application scenarios, this paper first elucidates differences across various multi-criteria prioritization methods regarding criteria substitutability, advantages and disadvantages, data input/output requirements, and applicable scenes. It further categorizes existing multi-criteria task prioritization methods into four main types: full aggregation methods, inferiority ranking methods, reference level methods, and data processing-based prioritization methods. Additionally, it discusses in detail the basic principles, implementation steps, latest research findings, and pros and cons of representative methods within each category. Finally, this paper summarizes current research hotspots, including the analytic hierarchy process, fuzzy theory, and AI-assisted ranking technologies, while suggesting future research pathways covering AI-assisted big data ranking optimization, ranking stability enhancement, and intelligent preference identification.

In response to the problems of current electromagnetic compatibility testing methods for elevator control cabinets, including low intelligence, cumbersome operation, and lack of elevator system status monitoring, this paper proposes an electromagnetic compatibility testing method for elevator control cabinets and designs a set of electromagnetic compatibility testing and control system for elevator control cabinets. The author constructed an "upper computer+ lower computer" control system architecture, developed an embedded controller based on ARM, designed application software for upper and lower computers of the automatic testing process and control system, implemented such functions as sensor signal acquisition and CAN bus communication for elevator control cabinets, and realized the real-time monitoring of electromagnetic compatibility testing of elevator control cabinets and elevator system status. Then, the author used this system to carry out the conducted immunity, voltage dip, voltage interruption, electrical fast transient pulse group and surge tests on elevator control cabinets, and conducted simulation tests on elevator control cabinets for sensor signal abnormalities, power supply failures, and communication anomalies. The results show that the electromagnetic compatibility testing and control system of the elevator control cabinet operates stably, with accurate data collection and simple operation, accurately and efficiently identifies various types of faults, meets the needs of elevator manufacturers and testing institutions for electromagnetic compatibility testing, and compared with previous testing methods, reduces testing manpower input by about 67%.

Electric submersible pump (ESP) oil production technology is widely used in non-flowing high-yield wells and high water-cut wells, but equipment faults are prone to occur during operation, and subsequent maintenance may trigger long-term downtime, which may cause incalculable economic losses. At present, ESP fault diagnosis mainly depends on the experience of field technicians, and automatic diagnosis and analysis cannot be done quickly and timely. Therefore, this paper proposes an ESP fault diagnosis model based on Laplacian eigenmaps and weighted extreme learning machine. In response to the serious imbalance in the data collected by ESP, firstly, a fault diagnosis model is established using a weighted extreme learning machine; Then, to solve the problems of insufficient algorithm learning, high computational costs caused by weighted strategies, and poor performance in applying to high-dimensional feature spaces, the Laplacian eigenmaps method is introduced to further optimize the model; finally, the effectiveness of the proposed method was validated on the TE chemical process dataset, and the practicality of the algorithm was experimentally validated on the real-time fault dataset of electric submersible pump. The results show that the classification average accuracy, maximum accuracy, and G-mean of the algorithm proposed in this paper are improved by more than 10% on average compared with those of the support vector machine, decision tree, backpropagation (BP) algorithm, extreme learning machine, and weighted extreme learning machine, thus confirming the effectiveness of the proposed method.

Periodic abnormal power fluctuations of wind turbines can easily cause low-frequency oscillations of the power grid, threatening the safety of the power grid. Wind turbine power signals are nonlinear and non-stationary signals, and their abnormal fluctuation Frequencies are uncertain, which makes it difficult to extract and detect abnormal power fluctuation features. This paper proposes a method for detecting abnormal power fluctuations of wind turbine based on subband processing and correlation coefficient. For the application of this method, the author first removed trend items of the original power signal by high-pass filter, and decomposed the signal by the subband processing filter bank to obtain a series of subband signals; calculated the cross-correlation coefficient between the power signal with trend items removed and each subband signal respectively, located the subband where periodic abnormal fluctuation occurred and filtered useful subband according to cross-correlation coefficients; obtained high signal-to-noise ratio signal reconstructed based on the filtered subband signal; and by combining the peak detection method and the autocorrelation analysis method, identified the time period where periodic abnormal fluctuations of specific amplitude of the reconstructed signal, thus realizing the detection of periodic abnormal power fluctuations of wind turbine. Field data tests show that for periodic abnormal power fluctuations of different frequencies such as 0.06Hz and 0.32Hz, the proposed method can effectively identify the subband where periodic abnormal fluctuations occur and the periodic abnormal fluctuation characteristics.

DC microgrid systems based on hybrid photovoltaic-electrical energy storage systems for power generation are widely used in new energy cruise ships. For the problem of power supply voltage unbalance arising when a daily load power supply converter is connected to an unbalanced load or single-phase load, this paper proposes a dead-beat predictive voltage control strategy based on positive and negative sequence separation of output voltage. This strategy can realize control of positive and negative sequence components of the power supply converter voltage under the positive and negative sequence coordinate systems respectively, to ensure the three-phase voltage balance of the power supply converter, enhance the anti-interference ability of the system against unbalanced load and avoid additional power quality control devices. The simulation results show that, after the proposed control strategy is adopted, the voltage unbalance of the daily load power supply converter is reduced from 28% to less than 0.5%, which verifies the effectiveness of the proposed control algorithm .

In analog sampling circuits, filters are essential for processing sampled signals before they are sent to DSP for further processing, to avoid clutter interference on them. This practice often reduces the hardware requirements for analog sampling channels. Compared to passive filters, active filters exhibit stable amplification and frequency characteristics with load changes. Therefore, they are more widely used in small signal processing circuits. However, designing high-order active filters often leads to discrepancies between theoretically calculated values and simulated outcomes, resulting in circuit parameters that fall short of the requirements of device selection. This study started from the analysis of second-order active filters, focusing on the transfer function, which revealed the cause of the above design annoyance, i.e. variations in the system damping coefficient. To address this challenge, two solutions were proposed to manipulate the system damping coefficient respectively by changing system gains and adjusting the ratio of the two capacitance values in filter circuits. Through further analysis and calculations, this study derived the system damping required for each second-order filter circuit within a high-order filter constructed using these two solutions. Furthermore, a PSPICE simulation analysis was performed using 4-order and 8-order filters with a cut-off frequency of 100 kHz designed following the proposed methodology. The resulting simulated cut-off frequency values of 98.828 kHz and 96.200 kHz, respectively, closely matched the theoretical calculations, verifying the effectiveness of the proposed design approach for high-order filters.

For the low parameter tuning efficiency, accuracy and adaptability of the traditional PID parameter tuning method currently adopted in the wind turbine variable-pitch system, this paper presents a method for self-optimization of wind turbine variable-pitch control parameters based on adaptive genetic algorithm, including variable-gain variable-pitch PID parameter self-optimization and tower damping parameter self-optimization. The author first described the basic principle of variable-pitch control system; then based on the genetic algorithm, planned and designed constraint conditions for different wind turbine models, covering initial population selection, self-optimization algorithm constraints and adaptability function design; and finally compared and analyzed the response curve, simulation and field operation results of the traditional parameter tuning method and self-optimization parameter tuning method. The results show that, compared with the traditional parameter tuning method, the self-optimization parameter tuning method reduces the motor speed fluctuation by about 10%, and the fatigue My load of the tower bottom by about 2%. This shows the self-optimization parameter tuning method can improve the tuning efficiency, accuracy and adaptability.

In order to solve the problems of difficult data integration, complex governance and low sharing efficiency, this paper proposes a scheme for construction of railway locomotive maintenance data integration and sharing platform. The author first realized the automatic and periodic data acquisition from different systems and saving of the same in a data warehouse through multi-source heterogeneous data acquisition technology, and then classified the data in the data warehouse for standardized management and forming service themes and thematic libraries. At the same time, the author also provided real-time and offline data sharing modes under security control, realizing data sharing in different scenarios. This platform, after being constructed in the field, saves more than 70% of the planned time for locomotive pre-scheduling and maintenance, and reduces the average downtime of locomotive maintenance by about 20 minutes. This shows the platform can help to promote multi-system data fusion and improve the informatization level, production efficiency and management ability of railway locomotive maintenance.

Unlike imaging in air, underwater panoramic imaging faces great challenges arising from water pressure and diminished light, necessitating special lens packaging and focused underwater illumination. The processing of underwater images from multiple channels necessitates adaptations to existing image processing algorithms to mitigate color cast, noisy points, and other imaging defects. And based on the hot research of panoramic video transmission, the underwater panoramic imaging system on the ROV platform holds great development potential.Based on the technical research of underwater panoramic photography, this paper first elaborates on the generation and transmission of underwater panoramic images and discusses two camera layouts: distributed and integrated, utilizing the remotely operated vehicle (ROV) platform as the carrier, through an analysis of camera hardware requirements. Subsequently, it introduces the creation of panoramic images from multiple channels of image data through a process involving image preprocessing, stitching, and fusion. Lastly, taking into account of remote video transmission through water boundaries, a self-adaptive multi-rate transmission scheme is presented, based on the stream division into slices (Tile stream) for panoramic videos. From the development of underwater panoramic technology, with the improvement of panoramic technologies such as lossless image stitching, iterative computing speed of image processors, image restoration, ultra bandwidth connection, and panoramic video transmission, ROV platform panoramic camera systems will be widely applied.

The accurate and fast localization of key components on high-speed railways in operation serves is one of the foundations for fault diagnosis of high-speed railways. However, utilizing ordinary industrial cameras for data collection on operational high-speed railways often results in motion blur or misses key components. On the other hand, training object detection models with data collected by ultra-high-speed spike cameras encounters challenges, such as difficulties in data annotation and model training. To this end, this paper proposes a detection method for key components on high-speed railways that leverages reconstruction based on spike data. Firstly, based on the principle of spike triggering, the light intensity was restored at different points following the inter-spike interval. Then, local adjustments to light intensity were made through global light intensity calculations. The adjusted results were further filtered and locally enhanced to reconstruct high-resolution grayscale images. Subsequently, after data cleaning and annotation based on the reconstructed images, the annotated data were used to train the detection model for key components on high-speed railways. Finally, the trained model was accelerated using TensorRT. Experimental results showed that the accelerated model achieved an accuracy of 98.7% and a single-frame inference rate of 3.5 ms both on average. The study findings lay a foundation for the engineering applications of the proposed algorithm .

In view of bottlenecks encountered in the process of turnout laying and replacement on high-speed railways such as substantial construction difficulties, low efficiency, intensive labor workload, and high risk, this paper proposes a control method of intelligent turnout laying and replacement equipment with combined gantry cranes for high-speed railways. It is proposed to utilize a control strategy of asynchronous transverse movement and synchronous tilting of the combined gantry cranes to handle the rotation and longitudinal motion of large components respectively. A theoretical mathematical modeling analysis is performed for the turnout laying and replacement equipment with combined gantry cranes focusing on the rotation and longitudinal movement operations. Moreover, considering the flexible nature of chains in the real construction process of combined gantry cranes, a self-learning algorithm is incorporated to facilitate flexible and highly precise multi-crane cooperative micro-motion control. The experimental results from field operations showcases adopting the control method proposed in this article, stable and reliable rotation and longitudinal movement by the combined gantry cranes, with an operational error of less than 10 mm, achieving full compliance with the requirements of engineering applications demonstrates.

High-speed automatic train operation (ATO) systems inherently exhibit strong nonlinearity and uncertainty. In view of the characteristics of nonlinearity and time-variability of high-speed train model parameters, this paper proposes a feed forward adaptive-generalized model predictive control (FA-GPC) method for dynamic optimization control of the ATO system along with a constrained multi-object predictive controller. Based on a multi-particle train model, an initial analysis explores the impacts of additional resistance changes on train operation. Subsequently, a multi-object performance indicator function containing control input constraints is constructed, combined with key indicators during the operation of high-speed trains, such as speed tracking accuracy, stopping accuracy, and riding comfort. Furthermore, a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function, aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates. Taking into account various factors including influences of external environments and passenger movement during train operation, the resistance changes greatly, making it difficult to establish an accurate mathematical model, a constrained variable forgetting factor-recursive least square method is incorporated to identify the controlled auto-regressive integrated moving average model (CARIMA) of the train control system under different operational conditions. This approach aims to improve the robustness of the control system. Simulation results show that, compared with traditional GPC in the absence of the feed forward functionality and PID controller, the proposed feed forward generalized controller demonstrates good cruise control speed tracking accuracy within a ±0.5 km/h range under different line conditions and strong robustness thanks to the adaptive modification to the feed forward generalized predictive control algorithm for better performance in strong disturbance conditions.

At present, the use of mobile LiDAR systems (MLS) to collect environmental information and generate roadside pole-shaped object inventories is limited by equipment costs and has poor real-time performance. Although LiDAR-based simultaneous localization and mapping (SLAM) techniques have been widely applied in the navigation field, research on simultaneous real-time localization and creation of roadside pole-shaped object inventories remains absent. In this regard, this paper proposes an approach that utilizes LiDAR technology to achieve vehicle localization and create roadside pole-shaped object inventories complete with absolute locations. The aim is to devise an accurate and robust system for vehicle localization and the creation of roadside pole-shaped object inventories. Firstly, LiDAR was integrated with inertial measurement units (IMU) and global navigation satellite system (GNSS), to achieve accurate pose estimations and simultaneous generation of global maps. Secondly, an optimized fusion positioning algorithm based on sliding windows is constructed, to enhance system robustness through effectively integrating data from multiple sensors. Then, a method for creating pole-shaped object inventories is proposed using an SLAM feature extraction algorithm, thereby reducing the computational expense for simultaneous vehicle positioning and roadside pole-shaped object inventory creation. Finally, extensive evaluations are conducted using real datasets covering various road scenarios, including urban and suburban areas. The experimental results demonstrated the centimeter-level vehicle positioning accuracy of the proposed system in real-time automatic creation of roadside pole-shaped object inventories, boasting an average positioning error within 3 cm.

The real-time transmission of online monitoring data for high-voltage transmission equipment in remote areas is a difficult problem in practical applications due to the lack of network. For this purpose, the article designs an online monitoring system for high-voltage transmission equipment based on ubiquitous IoT and LoRa technology. It constructs a low-power, long-distance LoRa wireless communication self-organizing network, transmits the transmission equipment status information and other data collected by front-end sensors to the LoRaWAN gateway, and then uploads the data to the back-end server through a 5G wireless network to achieve the overall communication link of the online monitoring system. The simulation experiment results show that compared with the existing technical solutions ZigBee, LoRa, and NB-IoT, using the scheme designed in the article, the system transmission speed (20-100 kbit/s) is the fastest, and the packet loss rate, transmission delay, and average time consumption (1.6%, 0.27s, and 60s, respectively) are the lowest. This indicates that the system not only has a fast transmission speed but also takes into account the characteristics of low power consumption and long distance, achieving stable data transmission, and is suitable for online monitoring of high-voltage transmission equipment in remote areas.

Freight transport with heavy trucks has emerged as a significant energy-intensive sector and a major contributor to carbon emissions in China's transportation industry. The diesel consumption in this field represents approximately 40 % of China's total annual diesel consumption, while the CO2 emissions account for approximately 50 % of the total annual CO2 emissions in the transportation industry. Under the background of carbon peaking and carbon neutrality goals and energy revolution, it has become an inevitable trend to build a green, secure and efficient road freight mode and promote the low-carbon and clean transformation of heavy freight transport. So far, the electrification options of freight transport with heavy trucks mainly include charging, battery swapping, hydrogen heavy trucks, and electrified highways. This paper mainly introduces the hydrogen heavy truck and electrified highway options. Based on a dedicated coal transport route, the economic efficiency and application scenarios were compared and analyzed between these two options from a perspective of cost estimates. The analysis results show that, for this dedicated coal transport route, when the annual traffic volume is greater than 2.0483 million tons, the electrified highway option is more economical, while on the contrary, the cost of the hydrogen heavy truck option is lower.

This paper aims to address compatibility among multiple types of encoding boards to streamline the design, construction, and maintenance processes of the automatic video surveillance and recording subsystem within locomotive on-board safety protection systems (referred to as the "6A video subsystem"). It proposes a design solution that uses middleware to shield the differences between underlying software and hardware. By analyzing and delving into aspects such as board identification, video stream acquisition, relationship decoupling, differential registration, and dynamic library loading, the design incorporates independent middleware to achieve compatibility among multiple types of video encoding boards. Verification and application results underscored the efficacy of the proposed solution in addressing dynamic replacements and compatibility across different types of video encoding boards without necessitating a restart of the 6A video subsystem, thereby improving operational efficiency in batch application and maintenance of the 6A video subsystem. Specifically, the troubleshooting time for a single locomotive was reduced to 1/3 compared to scenarios without this design. Moreover, the convenient interchange of multiple types of video encoding boards can significantly reduce system maintenance costs by eliminating the need for complete replacements of the 6A video subsystem.

When a diesel engine operates at non constant power output, it not only has low fuel efficiency but may also generate significant mechanical stress, leading to wear and damage of mechanical components. In order to ensure the efficient operation of diesels within diesel multiple units (DMU), this paper proposes a proportional-integral (PI) control approach based on the operational mode of the diesel generator sets featuring constant power output. This method was devised to steadily synchronize power of the traction systems on the load side with the target output power of the diesel generator sets. In turn, constant power operation on the load side guarantees a constant power output from the generator sets, thereby improving the fuel economy of the diesels. Through simulation and experimental tests, steady state fluctuations in load power were maintained within ±2%, demonstrating the effective constant power operation and the real-time alignment of load power with the target value.

The onboard sensor information of intelligent driving systems primarily fuses LiDAR and camera data. Accurate and stable extrinsic parameter calibrations offer the basis of effective multi-source information fusion. In order to improve the robustness of the perception system, this paper proposes an extrinsic calibration method for LiDAR and cameras based on feature matching. Firstly, a point cloud data sphere center algorithm and image data ellipse algorithm were proposed to extract point cloud three-dimensional coordinates and pixel two-dimensional coordinates of feature points. Next, the constraint of feature point pairs was established in the LiDAR and camera coordinate systems to construct a nonlinear optimization algorithm. Finally, this nonlinear optimization algorithm was used to optimize the extrinsic parameters of the LiDAR and cameras. Evaluation of the projected LiDAR point cloud onto the image based on the optimized extrinsic parameters yielded transverse and vertical average errors of 3.06 pixels and 1.19 pixels, respectively, in the LiDAR and camera joint calibration. The method proposed in the article reduces the average projection error by 40.8% and the error variance by 56.4% compared to the livox_camera_lidar_calibration method. Its accuracy and robustness are significantly better than the latter.

Aiming at the accurate speed tracking control of electric vehicles (EV) driven by permanent magnet synchronous motors (PMSM) under external load torque disturbances, this paper proposes an approach for prescribed performance back-stepping control based on a disturbance observer. Firstly, a controller was designed using the back-stepping technique, and a second-order sliding mode differentiator was introduced to approximate the derivative of the virtual control law, which can solve the "differential explosion" problem in traditional back-stepping control techniques. Secondly, in order to reduce the influence of motor load disturbances in the driving process of EV, a novel disturbance observer was proposed, bolstering the anti-interference capability of system. In addition, to achieve the preset high transient and steady-state speed tracking performance, a prescribed performance control strategy was incorporated to guarantee tracking errors within predefined boundaries. Finally, the Lyapunov function was utilized to verify the system stability. The simulation results showed that, compared with traditional PI control, the designed controller significantly increased motor speed tracking precision by more than 5 times, demonstrating a superior speed tracking performance and enhanced anti-interference capability.

This paper delves into the impact of abnormal echoes at the rail head on flaw recognition resulting from the central 70° probes on rail flaw detection (RFD) vehicles. Using B-scan data showcasing such 70° probe echoes near a transverse crack at the rail base on a rail flaw detection calibration track, this study elucidated the ultrasonic propagation path through analysis, theoretical calculations, and software simulation. The results identified these echoes as ghost echoes, originating from ultrasonic emissions by the central 70° probe and subsequently reflecting from the transverse crack at the rail base. Meanwhile, considering the characteristics of such ghost echoes, this paper presents practical suggestions for flaw recognition applications under the scenario of 70° echoes near a transverse crack at the rail base and a weld seam. The characteristics of such 70° probe echoes summarized in this paper can improve flaw recognition accuracy and reduce false alarming rates in rail detection.

In order to solve the issue of pantograph arcing on trains passing through neutral sections caused by excessive pressure differentials between both ends of these sections, a safety concern for train operation, this paper presents a voltage control system for flexible neutral section passing based on the line-level energy management system (EMS). The system obtains voltage control commands to control the energy dispatching equipment (EDE) by collecting the status information of the traction network, thereby achieving voltage balance between both ends to ensure smooth passage of trains through neutral sections. This study explored the principle of voltage regulation. A model for EMS voltage regulation was built to simulate the AC power supply system of Chongqing Rail Transit (CRT) Line 15. The simulation revealed that voltage control based on EMS effectively maintained voltage balance by reducing the voltage difference across neutral section ends by more than 20%. This study finding substantiates the efficacy and feasibility of the proposed control system.

The train control system, a safety-related component pivotal for ensuring the operational safety of trains, is prone to causing common cause failures (CCF) due to its multi-channel redundancy design. Hence, it is necessary to conduct a CCF analysis and implement safety protection for the train control system. Based on the system safety theory, potential common cause failures within the system were analyzed and evaluated using factor and independence analysis methods, and weak links were identified and examined in the system design process through a common cause failure checklist, leading to the proposed safety protection measures. Moreover, a quantitative evaluation was conducted on common cause failures based on a factor model to determine whether the probability of dangerous failure of the system within the acceptable range and aligns with the specified safety objectives. The article takes the computer interlocking system as the research object, and the quantitative calculation results indicated a reduced impact of common cause failures with a smaller factor. Consequently, it is recommended to incorporate appropriate safety protection measures in the system design to minimize the probability in the occurrence of common cause failures. By decreasing the factor value used in estimating system failures resulting from common cause failures, system safety is improved.

Automated parking stands as a cornerstone technology for autonomous vehicles operating in low-speed scenarios and has been extensively integrated into various vehicle models. Motion planning represents a pivotal technical module that generates planned trajectories to ensure vehicle movement from its initial pose to the target pose without collisions while aligning with kinematics rules, epitomizing the intelligence level of automated parking. In the context of ongoing transitions of automated driving technology from preliminary prototype research to industrial applications, automated parking technology in the realm of low-speed autonomous driving emerges as a critical area primed for breakthroughs. This paper offers a thorough review of the motion planning methods applied within the automated parking domain, categorizing them into geometric methods, sampling methods, search methods, numerical optimization methods, and machine learning methods. This paper also retrospectively examines their development and characteristics. Furthermore, this paper delves into the future directions of motion planning research for automated parking, with a particular focus on constructing more generalized models, enhancing the stability of planning methods, emphasizing the capabilities for online real-time high-quality re-planning and defensive driving, and addressing interactions with other traffic participants.

Distributed energy systems relying on renewable energy sources such as wind and solar energies are faced with challenges, including excessive frequency fluctuations and the complexity of hybrid energy storage control. To address these issues, the paper proposes a frequency control method based on Nash equilibrium quantum particle swarm optimization (NEQPSO) for distributed energy systems. Initially, a model is established to simulate distributed energy systems containing renewable energy sources and featuring hybrid energy storage. Subsequently, the outputs from both the slide mode controller (SMC) and the linear active disturbance rejection controller (LADRC) observer are integrated into the proportional derivative (PD) controller of the LADRC, leading to an improved slide mode-linear active disturbance rejection controller (SM-LADRC). Following this, NEQPSO is utilized to optimize the controller parameters to secure optimal configurations. Finally, comparative validation experiments are conducted under different settings, demonstrating that the optimized controller significantly outperformed conventional proportional integral derivative (PID) and proportional integral (PI) controllers. The optimization capability of NEQPSO proved clearly superior to that of particle swarm algorithm (PSO) and genetic algorithm (GA). Moreover, in subsequent on-load experiments and sensitivity validation experiments, distributed energy systems employing SM-LADRC based on NEQPSO exhibited swift responsiveness, strong disturbance rejection, improved frequency stability, and enhanced steady-state performance.